


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						RobertaTokenizer
							_UpperCAmelCase      =						RobertaTokenizerFast
							_UpperCAmelCase      =						True
							_UpperCAmelCase      =						{"""cls_token""": """<s>"""}






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										]
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					dict(zip(lowerCAmelCase__			,						range(len(lowerCAmelCase__       )       )       )       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
										SCREAMING_SNAKE_CASE_		:     str					=					{'unk_token': '<unk>'}

										SCREAMING_SNAKE_CASE_		:     Dict					=					os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['vocab_file']       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['merges_file']       )
										with open(self.vocab_file			,						'w'			,						encoding='utf-8'       ) as fp:
													fp.write(json.dumps(lowerCAmelCase__       ) + '\n'       )
										with open(self.merges_file			,						'w'			,						encoding='utf-8'       ) as fp:
													fp.write('\n'.join(lowerCAmelCase__       )       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										kwargs.update(self.special_tokens_map       )
										return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										kwargs.update(self.special_tokens_map       )
										return RobertaTokenizerFast.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					'lower newer'
										SCREAMING_SNAKE_CASE_		:     Any					=					'lower newer'
										return input_text, output_text






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self.tokenizer_class(self.vocab_file			,						self.merges_file			,						**self.special_tokens_map       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'lower newer'
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tokenizer.tokenize(lowerCAmelCase__       )  # , add_prefix_space=True)
										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					tokens + [tokenizer.unk_token]
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_tokenizer()

										self.assertListEqual(tokenizer.encode('Hello world!'			,						add_special_tokens=lowerCAmelCase__       )			,						[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]       )
										self.assertListEqual(
										    tokenizer.encode('Hello world! cécé herlolip 418'			,						add_special_tokens=lowerCAmelCase__       )			,						[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]			,						)






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.tokenizer_class.from_pretrained('roberta-base'       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer.encode('sequence builders'			,						add_special_tokens=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					tokenizer.encode('multi-sequence build'			,						add_special_tokens=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer.encode(
										    'sequence builders'			,						add_special_tokens=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer.encode(
										    'sequence builders'			,						'multi-sequence build'			,						add_special_tokens=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     str					=					tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__			,						lowerCAmelCase__       )

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'Encode this sequence.'
										SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer.byte_encoder[' '.encode('utf-8'       )[0]]

										# Testing encoder arguments
										SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer.encode(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     int					=					tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
										self.assertNotEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer.encode(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
										self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										tokenizer.add_special_tokens({'bos_token': '<s>'}       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					tokenizer.encode(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
										self.assertNotEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										# Testing spaces after special tokens
										SCREAMING_SNAKE_CASE_		:     Any					=					'<mask>'
										tokenizer.add_special_tokens(
										    {'mask_token': AddedToken(lowerCAmelCase__			,						lstrip=lowerCAmelCase__			,						rstrip=lowerCAmelCase__       )}       )  # mask token has a left space
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					'Encode <mask> sequence'
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					'Encode <mask>sequence'

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer.encode(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     int					=					encoded.index(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
										self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Any					=					tokenizer.encode(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					encoded.index(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
										self.assertNotEqual(lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																SCREAMING_SNAKE_CASE_		:     str					=					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     int					=					self.tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'A, <mask> AllenNLP sentence.'
																SCREAMING_SNAKE_CASE_		:     str					=					tokenizer_r.encode_plus(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer_p.encode_plus(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__       )

																# token_type_ids should put 0 everywhere
																self.assertEqual(sum(tokens_r['token_type_ids']       )			,						sum(tokens_p['token_type_ids']       )       )

																# attention_mask should put 1 everywhere, so sum over length should be 1
																self.assertEqual(
																    sum(tokens_r['attention_mask']       ) / len(tokens_r['attention_mask']       )			,						sum(tokens_p['attention_mask']       ) / len(tokens_p['attention_mask']       )			,						)

																SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']       )
																SCREAMING_SNAKE_CASE_		:     Dict					=					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']       )

																# Rust correctly handles the space before the mask while python doesnt
																self.assertSequenceEqual(tokens_p['input_ids']			,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )
																self.assertSequenceEqual(tokens_r['input_ids']			,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )

																self.assertSequenceEqual(
																    lowerCAmelCase__			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )
																self.assertSequenceEqual(
																    lowerCAmelCase__			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for trim_offsets, add_prefix_space in itertools.product([True, False]			,						repeat=2       ):
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.rust_tokenizer_class.from_pretrained(
													    self.tmpdirname			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )

													SCREAMING_SNAKE_CASE_		:     int					=					json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()       )

													self.assertEqual(pre_tokenizer_state['add_prefix_space']			,						lowerCAmelCase__       )

													self.assertEqual(post_processor_state['add_prefix_space']			,						lowerCAmelCase__       )
													self.assertEqual(post_processor_state['trim_offsets']			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
																SCREAMING_SNAKE_CASE_		:     Tuple					=					F'''{text_of_1_token} {text_of_1_token}'''

																SCREAMING_SNAKE_CASE_		:     Tuple					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Dict					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(len(lowerCAmelCase__       ) + 1, len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     List[str]					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(len(lowerCAmelCase__       ) + 1, len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     List[str]					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(len(lowerCAmelCase__       ), len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     Any					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(len(lowerCAmelCase__       ), len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     Any					=					F''' {text}'''

																# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																# )
																# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																# self.assertEqual(
																#     encoding.offset_mapping[1],
																#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																# )

																SCREAMING_SNAKE_CASE_		:     int					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(1, 1 + len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(1 + len(lowerCAmelCase__       ) + 1, 1 + len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     int					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, 1 + len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(1 + len(lowerCAmelCase__       ), 1 + len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     Dict					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, 1 + len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(1 + len(lowerCAmelCase__       ), 1 + len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)













"""simple docstring"""





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					DebertaTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					True
    SCREAMING_SNAKE_CASE		:	Any		=					DebertaTokenizerFast






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Dict:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      lowerCamelCase_       	=		[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '[UNK]',
      ]
      lowerCamelCase_       	=		dict(zip(__SCREAMING_SNAKE_CASE			,		range(len(__SCREAMING_SNAKE_CASE   )   )   )   )
      lowerCamelCase_       	=		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      lowerCamelCase_       	=		{'unk_token': '[UNK]'}

      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file']   )
      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['merges_file']   )
      with open(self.vocab_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write(json.dumps(__SCREAMING_SNAKE_CASE   ) + '\n'   )
      with open(self.merges_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write('\n'.join(__SCREAMING_SNAKE_CASE   )   )






    def   UpperCamelCase			(			self	:				Tuple			,		**__SCREAMING_SNAKE_CASE	:				Dict   )	->						List[Any]:
      kwargs.update(self.special_tokens_map   )
      return self.tokenizer_class.from_pretrained(self.tmpdirname			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Dict:
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		'lower newer'
      return input_text, output_text






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
      lowerCamelCase_       	=		tokenizer.tokenize(__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokens + [tokenizer.unk_token]
      lowerCamelCase_       	=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		tokenizer('Hello'			,		'World'   )
      lowerCamelCase_       	=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
      self.assertListEqual(tokd['token_type_ids']			,		__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.tokenizer_class.from_pretrained('microsoft/deberta-base'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		'multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      lowerCamelCase_       	=		[self.tokenizer_class]
      if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class   )

      for tokenizer_class in tokenizer_classes:
        lowerCamelCase_       	=		tokenizer_class.from_pretrained('microsoft/deberta-base'   )

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        lowerCamelCase_       	=		tokenizer(__SCREAMING_SNAKE_CASE			,		padding=__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		[tokenizer.decode(__SCREAMING_SNAKE_CASE			,		skip_special_tokens=__SCREAMING_SNAKE_CASE   ) for seq in encoding['input_ids']]

        # fmt: off
        lowerCamelCase_       	=		{
            'input_ids': [
                [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
            ],
            'token_type_ids': [
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
            ],
            'attention_mask': [
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
            ]
        }
        # fmt: on

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        self.assertDictEqual(encoding.data			,		__SCREAMING_SNAKE_CASE   )

        for expected, decoded in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          self.assertEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




